• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.607-614
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• 2000

A cost-effective automated slope monitoring system is developed to monitor hazardous cut slopes along highways. This automated slope monitoring system consists of data-collection and visual monitoring, data-transmitting, database and internet service, and alarm system. Wire-line extensometer, automatic raingauge, and CCD camera are selected as monitoring instruments in this system, after consideration of failure characteristics of roadside cut slopes in the country. This paper describes the important features of this newly developed automated slope monitoring system.

• Tunnel and Underground Space
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• v.16 no.6 s.65
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• pp.451-466
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• 2006

Analysis of slope behavior concerning the structural characteristics of field rock mass can be processed by virtue of borehole information of joint orientation and position acquired from DOM drilled core. Anticipated sliding potential of pre-failed rock slope is analyzed and the regional slope instability is investigated by inspecting the hazardous joints and blocks the traces of which is projected on the cut-face. Cross section has been set at the center of rock slope and the traces of both joints and tetrahedral blocks, which potentially can induce the slope failure, are drawn to investigate the failure modes and the triggering mechanism. Automated monitoring system has been established to measure the slope movement and especially, inclinometer has been installed inside DOM borehole to analyze the slope movement by considering the internal rock structure. Algorithms for predicting the slope failure time have been reviewed and the significance of heavy rainfall on the slope behavior has been investigated.

• ETRI Journal
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• v.42 no.2
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• pp.217-229
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• 2020

Long-term electroencephalography (EEG) monitoring is time-consuming, and requires experts to interpret EEG signals to detect seizures in patients. In this paper, we propose a novel automated method called adaptive slope of wavelet coefficient counts over various thresholds (ASCOT) to classify patient episodes as seizure waveforms. ASCOT involves extracting the feature matrix by calculating the mean slope of wavelet coefficient counts over various thresholds in each frequency subband. We validated our method using our own database and a public database to avoid overtuning. The experimental results show that the proposed method achieved a reliable and promising accuracy in both our own database (98.93%) and the public database (99.78%). Finally, we evaluated the performance of the method considering various window sizes. In conclusion, the proposed method achieved a reliable seizure detection performance with a short-term window size. Therefore, our method can be utilized to interpret long-term EEG results and detect momentary seizure waveforms in diagnostic systems.

• Journal of Biosystems Engineering
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• v.36 no.5
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• pp.326-333
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• 2011

The need for rapid on-site monitoring of hydroponic macronutrients has led to the use of ion-selective electrodes, because of their advantages over spectrophotometric methods, including simple methodology, direct measurement of analyte, sensitivity over a wide concentration range, and low cost. Stability and repeatability of response can be a concern when using multiple ion-selective electrodes to measure concentrations in a series of samples because accuracy might be limited by drifts in electrode potential. A computer-based measurement system could improve accuracy and precision because of both consistent control of sample preparation and easy calibration of sensors. Our goal was to investigate the applicability of a cobalt-based electrode used in conjunction with a laboratory-made automated test stand for quantitative determination of ${PO_4}^-$ in hydroponic solution. Six hydroponic solutions were prepared by diluting highly concentrated paprika hydroponicsolution to provide a concentration range of 1 to 300 ppm $PO_4$-P. A calibration curve relating electrode response to phosphate in paprika hydroponic solution titrated to pH 4 with 0.025M KHP was developed based on the Nikolskii-Eisenman equation with a coefficient of determination ($R^2$) of 0.94. The laboratory-made test stand consisting of three cobalt-based electrodes measured phosphate concentrations similar to those obtained with standard laboratory methods (a regression slope of 0.98 with $R^2$ = 0.80). However, the y intercept was relatively high, 30 ppm, probably due to the relatively large amount of variation present among multiple measurements of the same sample. Further studies on the high variation in EMFs obtained with cobalt electrodes during replicate measurements were required for P estimations comparable to those obtained with standard laboratory instruments.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.45-45
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• 2017

On-site monitoring of vegetable growth parameters, such as leaf length, leaf area, and fresh weight, in an agricultural field can provide useful information for farmers to establish farm management strategies suitable for optimum production of vegetables. Unmanned Aerial Vehicles (UAVs) are currently gaining a growing interest for agricultural applications. This study reports on validation testing of previously developed vegetable growth estimation models based on UAV-based RGB images for white radish and Chinese cabbage. Specific objective was to investigate the potential of the UAV-based RGB camera system for effectively quantifying temporal and spatial variability in the growth status of white radish and Chinese cabbage in a field. RGB images were acquired based on an automated flight mission with a multi-rotor UAV equipped with a low-cost RGB camera while automatically tracking on a predefined path. The acquired images were initially geo-located based on the log data of flight information saved into the UAV, and then mosaicked using a commerical image processing software. Otsu threshold-based crop coverage and DSM-based crop height were used as two predictor variables of the previously developed multiple linear regression models to estimate growth parameters of vegetables. The predictive capabilities of the UAV sensing system for estimating the growth parameters of the two vegetables were evaluated quantitatively by comparing to ground truth data. There were highly linear relationships between the actual and estimated leaf lengths, widths, and fresh weights, showing coefficients of determination up to 0.7. However, there were differences in slope between the ground truth and estimated values lower than 0.5, thereby requiring the use of a site-specific normalization method.